Unmanned ground vehicles (UGVs) include remote-driven or self-driven land vehicles that can carry cameras, sensors, communications equipment, or other payloads. Self-driven or “autonomous” land vehicles are essentially robotic platforms that are capable of operating outdoors and over a wide variety of terrain.
Autonomous land vehicles can travel at various speeds under diverse road constructs. For example, an autonomous land vehicle can travel at the speed limit when traffic is sparse, at low speed during a traffic jam, or can stop at a traffic light. The autonomous land vehicle can also travel at a constant speed, as well as accelerate or decelerate. The road on which the vehicle traverses can be straight, curved, uphill, downhill, or have many undulations. The number of lanes on the road can vary, and there are numerous types of road side constructs such as curbs, lawns, ditches, or pavement. Objects on and off the road such as cars, cycles, and pedestrians add more complexity to the scenario. It is important to accurately classify these road elements in order that the vehicle can navigate safely.
Numerous sensors are typically mounted on board of autonomous land vehicles to aid in navigation. Some of these sensors include global positioning system (GPS) and inertial navigation system (INS) sensors, radar, video and IR cameras, and laser detection and ranging (LADAR) sensors. LADAR is also referred to as LIDAR (Light Detection and Ranging), typically in non-military contexts.
The navigation systems for autonomous land vehicles often have difficulty in processing the LADAR data and combining the GPS/INS data to accurately classify each range/reflectance measurement in a scan into one of traversable, non-traversable, lane-mark, and obstacle classes. Classification of the range measurements based only on one input scan and its corresponding GPS/INS input is not robust enough with the diversity of vehicle states and road configurations that can be encountered.
In some navigation systems, each range measurement in a scan is classified based on a history of recent range scans. A fixed-size history buffer is employed having a size based on a fixed number of range scans. Consequently, the distance covered by the range scans saved in this buffer depends on the speed of the vehicle. When the vehicle travels at high speed, the area coverage in a fixed number of scans is large. When the vehicle travels at slow speed, the area coverage is small. Using the scans in the fixed-size buffer for ground plane estimation causes varying degrees of inaccuracy.
In addition to accurately knowing the road versus the non-traversable areas, autonomous vehicle navigation systems would also be benefited by knowing the markings on the roads such as lane marks, which would allow for lane following and/or lane changing maneuvers. While some LADAR systems provide reflectance measurements along with range measurements, current navigation systems do not provide for using reflectance measurements to derive markings on the road.